Processing facilities, such as manufacturing plants, chemical plants and oil refineries, are typically managed using process control systems. Valves, pumps, motors, heating/cooling devices, and other industrial equipment typically perform actions needed to process materials in the processing facilities. Among other functions, the process control systems often manage the use of the industrial equipment in the processing facilities.
In conventional process control systems, controllers are often used to control the operation of the industrial equipment in the processing facilities. The controllers can typically monitor the operation of the industrial equipment, provide control signals to the industrial equipment, and/or generate alarms when malfunctions are detected. Process control systems typically include one or more process controllers and input/output (I/O) devices communicatively coupled to at least one workstation and to one or more field devices, such as through analog and/or digital buses. The field devices can include sensors (e.g., temperature, pressure and flow rate sensors), as well as other passive and/or active devices. The process controllers can receive process information, such as field measurements made by the field devices, in order to implement a control routine. Control signals can then be generated and sent to the industrial equipment to control the operation of the process.
Advanced controllers often use model-based control techniques to control the operation of the industrial equipment. Model-based control techniques typically involve using an empirical model to analyze input data, where the model identifies how the industrial equipment should be controlled based on the input data being received.
Model predictive controllers (MPCs) rely on dynamic models of the process, most often linear empirical models obtained by system identification. The models are used to predict the behavior of dependent variables (e.g. outputs) of a dynamic system with respect to changes in the process independent variables (e.g. inputs). In chemical processes, independent variables are most often setpoints of regulatory controllers that govern valve movement (e.g., valve positioners with or without flow, temperature or pressure controller cascades), while dependent variables are most often constraints in the process (e.g., product purity, equipment safe operating limits). The model predictive controller uses the models and current plant measurements to calculate future moves in the independent variables that will result in operation that attempts to satisfy all independent and dependent variable constraints. The MPC then sends this set of independent variable moves to the corresponding regulatory controller setpoints to be implemented in the process.
One such model-based control technique utilizes a linear MPC for control of a non-linear process with complex constrained multivariable control issues. At each sampling time (starting at the current state), an open-loop optimal control problem is solved over a finite horizon. At the next time step, the computation is repeated starting from the new state and over a shifted horizon, leading to a moving horizon policy. Thus, the control process linearizes around each operating point, by recalculating the model corresponding to current operating regions. However, linearizing at all steps of the process to be controlled to obtain a conventional MPC solution, greatly increases the computational load for the control system. This can be especially significant for more complex processes with large numbers of operational parameters and variables.
Accordingly, there is a need for a method and system for process control that can provide an operator with an understanding of the extent of non-linearity of the process to be controlled. There is a further need for such a method and system that allows for obtaining acceptable control performance while minimizing computational time or load.